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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.

Topic 2

Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.

Topic 3

Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.

Topic 4

Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q78-Q83):

NEW QUESTION # 78
Which is a key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Reduced model complexity
B. Faster training time and lower cost
C. Enhanced generalization to unseen data
D. Increased model interpretability

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning method, updates fewer parameters than Vanilla fine-tuning, leading to faster training and lower computational costs-Option D is correct. Option A (complexity) isn't directly affected-structure remains. Option B (generalization) may occur but isn't the primary advantage. Option C (interpretability) isn't a focus. Efficiency is T-Few's hallmark.
OCI 2025 Generative AI documentation likely compares T-Few and Vanilla under fine-tuning benefits.

NEW QUESTION # 79
Why is it challenging to apply diffusion models to text generation?

A. Because text representation is categorical unlike images
B. Because text generation does not require complex models
C. Because text is not categorical
D. Because diffusion models can only produce images

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false-text generation can benefit from complex models. Option B is incorrect-text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.

NEW QUESTION # 80
What is the purpose of memory in the LangChain framework?

A. To store various types of data and provide algorithms for summarizing past interactions
B. To act as a static database for storing permanent records
C. To perform complex calculations unrelated to user interaction
D. To retrieve user input and provide real-time output only

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.

NEW QUESTION # 81
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?

A. It limits their ability to understand and generate natural language.
B. It transforms their architecture from a neural network to a traditional database system.
C. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
D. It enables them to bypass the need for pretraining on large text corpora.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG integrates vector databases to retrieve real-time external data, augmenting the LLM's pretrained knowledge with current, specific information, shifting response generation to a hybrid approach-Option B is correct. Option A is false-architecture remains neural; only data sourcing changes. Option C is incorrect-pretraining is still required; RAG enhances it. Option D is wrong-RAG improves, not limits, generation. This shift enables more accurate, up-to-date responses.
OCI 2025 Generative AI documentation likely details RAG's impact under responsegeneration enhancements.

NEW QUESTION # 82
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

A. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
B. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
C. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
D. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.

NEW QUESTION # 83
......

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